6b6172d8f0
- 添加本地 FAQ 库快速路径（问候语等社交响应） - 修复 Chatwoot 重启循环问题（PID 文件清理） - 添加 LLM 响应缓存（Redis 缓存，提升性能） - 添加智能推理模式（根据查询复杂度自动启用） - 添加订单卡片消息功能（Chatwoot 富媒体） - 增加 LLM 超时时间至 60 秒 Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
228 lines
8.0 KiB
Python
228 lines
8.0 KiB
Python
"""
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Customer Service Agent - Handles FAQ and general inquiries
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"""
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import json
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from typing import Any
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from core.state import AgentState, ConversationState, add_tool_call, set_response
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from core.llm import get_llm_client, Message
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from prompts import get_prompt
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from utils.logger import get_logger
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from utils.faq_library import get_faq_library
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logger = get_logger(__name__)
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async def customer_service_agent(state: AgentState) -> AgentState:
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"""Customer service agent node
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Handles FAQ, company info, and general inquiries using Strapi MCP tools.
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Args:
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state: Current agent state
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Returns:
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Updated state with tool calls or response
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"""
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logger.info(
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"Customer service agent processing",
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conversation_id=state["conversation_id"]
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)
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state["current_agent"] = "customer_service"
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state["agent_history"].append("customer_service")
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state["state"] = ConversationState.PROCESSING.value
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# Check if we have tool results to process
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if state["tool_results"]:
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return await _generate_response_from_results(state)
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# ========== FAST PATH: Check if FAQ was already matched at router ==========
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# Router already checked FAQ and stored response if found
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if "faq_response" in state and state["faq_response"]:
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logger.info(
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"Using FAQ response from router",
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conversation_id=state["conversation_id"],
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response_length=len(state["faq_response"])
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)
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return set_response(state, state["faq_response"])
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# =========================================================================
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# ========== FAST PATH: Check local FAQ library first (backup) ==========
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# This provides instant response for common questions without API calls
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# This is a fallback in case FAQ wasn't matched at router level
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faq_library = get_faq_library()
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faq_response = faq_library.find_match(state["current_message"])
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if faq_response:
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logger.info(
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"FAQ match found, returning instant response",
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conversation_id=state["conversation_id"],
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response_length=len(faq_response)
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)
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return set_response(state, faq_response)
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# ============================================================
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# Get detected language
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locale = state.get("detected_language", "en")
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# Auto-detect category and query FAQ
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message_lower = state["current_message"].lower()
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# 定义分类关键词
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category_keywords = {
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"register": ["register", "sign up", "account", "login", "password", "forgot"],
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"order": ["order", "place order", "cancel order", "modify order", "change order"],
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"payment": ["pay", "payment", "checkout", "voucher", "discount", "promo"],
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"shipment": ["ship", "shipping", "delivery", "courier", "transit", "logistics", "tracking"],
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"return": ["return", "refund", "exchange", "defective", "damaged"],
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}
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# 检测分类
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detected_category = None
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for category, keywords in category_keywords.items():
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if any(keyword in message_lower for keyword in keywords):
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detected_category = category
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break
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# 检查是否已经查询过 FAQ
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tool_calls = state.get("tool_calls", [])
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has_faq_query = any(tc.get("tool_name") in ["query_faq", "search_knowledge_base"] for tc in tool_calls)
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# 如果检测到分类且未查询过 FAQ，自动查询
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if detected_category and not has_faq_query:
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logger.info(
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f"Auto-querying FAQ for category: {detected_category}",
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conversation_id=state["conversation_id"]
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)
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# 自动添加 FAQ 工具调用
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state = add_tool_call(
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state,
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tool_name="query_faq",
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arguments={
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"category": detected_category,
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"locale": locale,
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"limit": 5
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},
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server="strapi"
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)
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state["state"] = ConversationState.TOOL_CALLING.value
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return state
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# 如果询问营业时间或联系方式，自动查询公司信息
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if any(keyword in message_lower for keyword in ["opening hour", "contact", "address", "phone", "email"]) and not has_faq_query:
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logger.info(
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"Auto-querying company info",
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conversation_id=state["conversation_id"]
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)
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state = add_tool_call(
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state,
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tool_name="get_company_info",
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arguments={
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"section": "contact",
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"locale": locale
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},
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server="strapi"
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)
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state["state"] = ConversationState.TOOL_CALLING.value
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return state
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# Build messages for LLM
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# Load prompt in detected language
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system_prompt = get_prompt("customer_service", locale)
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messages = [
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Message(role="system", content=system_prompt),
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]
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# Add conversation history
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for msg in state["messages"][-6:]:
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messages.append(Message(role=msg["role"], content=msg["content"]))
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# Add current message
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messages.append(Message(role="user", content=state["current_message"]))
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try:
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llm = get_llm_client()
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response = await llm.chat(messages, temperature=0.7)
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# Parse response
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content = response.content.strip()
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if content.startswith("```"):
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content = content.split("```")[1]
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if content.startswith("json"):
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content = content[4:]
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result = json.loads(content)
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action = result.get("action")
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if action == "call_tool":
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# Add tool call to state
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state = add_tool_call(
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state,
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tool_name=result["tool_name"],
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arguments=result.get("arguments", {}),
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server="strapi"
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)
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state["state"] = ConversationState.TOOL_CALLING.value
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elif action == "respond":
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state = set_response(state, result["response"])
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state["state"] = ConversationState.GENERATING.value
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elif action == "handoff":
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state["requires_human"] = True
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state["handoff_reason"] = result.get("reason", "User request")
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return state
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except json.JSONDecodeError:
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# LLM returned plain text, use as response
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state = set_response(state, response.content)
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return state
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except Exception as e:
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logger.error("Customer service agent failed", error=str(e))
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state["error"] = str(e)
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return state
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async def _generate_response_from_results(state: AgentState) -> AgentState:
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"""Generate response based on tool results"""
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# Build context from tool results
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tool_context = []
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for result in state["tool_results"]:
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if result["success"]:
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tool_context.append(f"Tool {result['tool_name']} returned:\n{json.dumps(result['data'], ensure_ascii=False, indent=2)}")
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else:
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tool_context.append(f"Tool {result['tool_name']} failed: {result['error']}")
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prompt = f"""Based on the following tool returned information, generate a response to the user.
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User question: {state["current_message"]}
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Tool returned information:
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{chr(10).join(tool_context)}
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Please generate a friendly and professional response. If the tool did not return useful information, honestly inform the user and suggest other ways to get help.
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Return only the response content, do not return JSON."""
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messages = [
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Message(role="system", content="You are a professional B2B customer service assistant, please answer user questions based on tool returned information."),
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Message(role="user", content=prompt)
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]
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try:
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llm = get_llm_client()
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response = await llm.chat(messages, temperature=0.7)
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state = set_response(state, response.content)
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return state
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except Exception as e:
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logger.error("Response generation failed", error=str(e))
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state = set_response(state, "Sorry, there was a problem processing your request. Please try again later or contact customer support.")
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return state
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